Methodology, Measurement

We explore the use of instrumental variables (IV) analysis with a multi-site randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, as assumption known in the instrumental variables literature as the exclusion restriction.

This study assesses a new measure of earlychildhoodclassroom practice in 127 kindergarten- and first-grade classrooms. The measure was designed to be appropriate for classrooms serving children from the age of 4–7 years. It assesses the nature and quality of instruction as well as the social climate and management of the classroom. Two separate scales assess the degree to which constructivist, child-centered and the degree to which didactic, teacher-centered instructional practices are implemented.

A measure of young children's generalized tendency to expect positive or negative outcomes (Optimism-Pessimism Test Instrument : OPTI) is described. Descriptive data and evidence for the measure's reliability for first- and second-grade children are provided. Validity is assessed by the measure's relationship to several other measures of personality constructs. Moderate but significant correlations were found between OPTI and attitude toward school, self-concept, delay of gratification, and locus of control.

One reason for the recent proliferation of empirical studies addressing the role of education in promoting individual and social welfare has been the emergence of high-quality, micro-level data on education. In this paper, we discuss three broad types of data sets: data on educational institutions, data on educational outcomes at the individual level, and data sets from school reforms and experiments.

The last decade has seen a surge in empirical research byeconomists addressing the impact of school reform poli-cies. This wave of research dates back to Card and Krueger(1992) and a series of analyses that look across the UnitedStates to test the effects of school inputs on outcomes such asachievement, educational attainment, and earnings. Many ofthese studies use either Census data (Heckman, Layne–Farrar,and Todd, 1996) or national longitudinal surveys (Betts 1996;Loeb and Bound, 1996; Grogger, 1996) to estimate input ef-fects.

The census tract—based residential segregation literature rests on problematic assumptions about geographic scale and proximity. We pursue a new tract-free approach that combines explicitly spatial concepts and methods to examine racial segregation across egocentric local environments of varying size. Using 2000 Census data for the 100 largest U.S. metropolitan areas, we compute a spatially modified version of the information theory index H to describe patterns of Black—White, Hispanic-White, Asian-White, and multigroup segregation at different scales.

Purpose – To develop measures of segregation that are appropriate when either the groups or the organizational units are defined by ordered categories. These methods allow the measurement of segregation among groups defined by ordered educational attainment categories or among ordered occupational categories, for example.

Approach – I define a set of desirable properties of such measures, develop a general approach to constructing such measures, derive three such measures, and show that these measures satisfy the required properties.

The ability of school (or teacher) value‐added models to provide unbiased estimates of school (or teacher) effects rests on a set of assumptions. In this paper, we identify six assumptions that are required in order that the estimands of such models are well‐defined and that the models are able to recover the desired parameters from observable data. These assumptions are 1) manipulability; 2) no interference between units; 3) interval scale metric; 4) homogeneity of effects; 5) strongly ignorable assignment; and 6) functional form.